Presentation shared by author at the 2019 EDEN Annual Conference "Connecting through Educational Technology" held on 16-19 June, 2019 in Bruges, Belgium. Find out more on #eden19 here: http://www.eden-online.org/2019_bruges/

1. AI in Education
State of Practice and
Paths Toward the Future
Ilkka Tuomi
ilkka.tuomi@meaningprocessing.com

2. “AI will be the main driver of economic and productivity
growth and will contribute to the sustainability and
viability of the industrial base in Europe. Like the steam
engine or electricity in the past, AI is transforming the
world.”
European Commission. December, 2018. “Coordinated Plan on Artificial
Intelligence.”

3. “There is no doubt that AI will revolutionize the
delivery and management of education and learning, ...
And while we believe that teachers will not be replaced by
machines by 2030, we still need a dynamic review of how
AI will transform teachers’ roles.”
UNESCO, April, 2019

4. Revolution?
Education?
Artificial intelligence?
Learning? Transforming?

5. “civilizing” “developing”
●
Inter-generational transfer ●
Capability, growth, agency
●
Reproduction of social and
techno-economic structures
The eternal tension

6. Capability and agency
“Knowledge”
What = “K”
How = “S”
When/Why = “E”
Attitude
Motivation
Behavioral repertoire
Social resources
Material resources
*

7. Framing the problem
●
Human intelligence is socially and materially distributed
– Ask not what’s inside your head, but what your head’s inside of.
(Gibson)
– It’s not what you know, it’s who you know. (Nardi)
●
Technologies become real when they change social practices
●
All innovation is social innovation
●
“Electricity” did not revolutionize the world. We did, and used
a lot of electricity, steam and coal in so doing.

9. Executive summary
”This policy foresight report suggests that in the next years AI
will change learning, teaching, and education. The speed of
technical change will be very fast, and it will create high
pressure to transform educational practices, institutions, and
policies.
It is therefore important to understand the potential impact of
AI on learning, teaching, and education, as well as on policy
development.”
(p.2)

10. To understand systemic change, we need to
understand the current system
●
Why the educational system is what it is today?
●
Why did people have jobs in the past?
●
Why did they need to learn skills and knowledge?

11. Why growth and jobs in the past?
●
In the 20th century, innovation focused on areas where
increased energy use was able to reduce human labor
– We borrowed from the nature without intent to pay back
●
Known as “non-renewable resources” and “fossil fuels”
●
No labor costs, no pension payments, no social security for anaerobic
bacteria that made all this possible
●
This replacement of renewable energy with fossil fuels is
know as “automation,” “mechanization of work” and, in
economics, as “labor productivity growth,” and “technology”

12. The real source of growth in the Industrial Age
The world production of cement was about 4.1 billion tons in 2017. This translates to
15,000 petajoules, or about 2,478,283,594 equivalent barrels of oil.

13. “Miracles You'll See In The Next Fify Years”
Popular Mechanics, 1950
Because everything in her home is waterproof, the housewife of 2000 can do her daily cleaning with a hose.

14. Middle school teachers...”other things being equal”
Task AI
impact
1 Adapt teaching methods and instructional materials to meet students' varying needs and interests. High
2 Establish and enforce rules for behavior and procedures for maintaining order among students. ?
3 Confer with parents or guardians, other teachers, counselors, and administrators to resolve
students' behavioral and academic problems.
Low
4 Maintain accurate, complete, and correct student records as required by laws, district policies, and
administrative regulations.
High
5 Prepare, administer, and grade tests and assignments to evaluate students' progress. High
6 Prepare materials and classrooms for class activities. Medium
7 Instruct through lectures, discussions, and demonstrations in one or more subjects, such as
English, mathematics, or social studies.
Medium
8 Establish clear objectives for all lessons, units, and projects, and communicate these objectives to
students.
Medium
9 Assist students who need extra help, such as by tutoring and preparing and implementing remedial
programs.
High
10 Assign lessons and correct homework. High
11 Enforce all administration policies and rules governing students. Medium
...
15 Meet or correspond with parents or guardians to discuss children's progress and to determine
priorities and resource needs.
Medium
source: O*NET and author’s estimates

15. Three types of AI
●
Logic-based; algorithmic
– 1955-1975: Computer is a universal logical machine; it is able to reason as well as
humans
●
Knowledge-based; representational
– 1970-1990: Intelligent behavior depends on knowing the world; the question is about
representing knowledge and its structures
●
Data-driven
– 1930-1965 --- 1985-1990 --- 2005-present
– Machine learning algorithms that minimize prediction or clustering error
– Known as artificial neural networks and machine learning

16. A new paradigm for programming
●
Machine Learning is a diferent paradigm: instead of telling how to do things, you tell what the goal is.
●
Requires examples (ofen millions of them).
– The widely-used ImageNet database employed at its peak 48,940 people in 167 countries who sorted 14 million
images
– Google BERT Language model (2018): trained 340 million parameters four days with 5x1015 floating point
operations per second
●
2x1021 computations to make a state-of-the-art machine learning model
●
3.3 billion words used for training
●
Requires cheap parallel processing and new hardware architectures
●
Requires data
– It’s all about Internet connectivity
●
Enabled by open source development tools and freely accessible”pre-trained” machine learning
models (from Google et al.)

17. Rosenblatt’s Perceptron, 1958
“Fire together, wire together”

18. GoogLeNet, 2014

19. 8 million web pages, 1.5 billion trainable parameters:
OpenAIGPT-2,February2019

20. Algorithmic computing
●
‘You insist that there is something a machine cannot do. If
you will tell me precisely what it is that a machine cannot do,
I can always make a machine which will do just that’.
– Von Neumann, 1963, ”The general and logical theory of automata.”

21. Data-driven computing
●
‘If a typical person can do a mental task with less than one
second of thought, we can probably automate it using AI
either now or in the near future.’
– Andrew Ng, HBR, 2016

22. What, exactly, is machine learning?
Top: Cultural
Middle: Cognitive
Bottom: Behavioral
socialphysiological
Perception,Attention,Emotion
Practice
Institution
Value
Sign
Concept
Word
Habit
Reflex
Routine
Piaget
Vygotsky Freire
How culture
makes us
How we
make the culture
Skinner
Anything here can be done with less than one second of thinking.

23. What, exactly, is machine learning?
Top: Cultural
Middle: Cognitive
Bottom: Behavioral
socialphysiological
Perception,Attention,Emotion
Anything here can be done with less than one second of thinking.
Symbol-processing AI
Deep learning
Innovation and knowledge
creation

24. Yes, AI can make magic
●
But all its “intelligence” comes from humans
●
Most current machine learning systems are based on human-
labeled data examples (e.g. face recognition, object
detection, question-answering systems...)
●
...or statistical regularities in human generated text, speech,
movement, art
●
...or closed rule-based worlds where machines can generate
training data (chess, go…)
●
...or combination of these (e.g. deepfake videos...)

25. It sees what it has learned
Lev Vygotsky, ~1928

26. Google Inception trained with ImageNet data

27. AI at School
Student teaching
(instructivist)
Student supporting
(constructivist)
Teacher supporting System supporting
●
Intelligent tutoring
systems (including
automatic question
generators and brain-
based teaching)
●
Dialogue-based tutoring
systems
●
Language learning
applications (including
pronounciation detection)
●
Exploratory learning
environments
●
Formative writing
evaluation
●
Learning network
orchestrators
●
Language learning
applications
●
AI Collaborative learning
●
AI Continuous assessment
●
AI Learning companions
●
Course recommendation
●
Self-reflection support
(learning analytics, meta-
cognitive dashboards)
●
Learning by teaching
chatbots
●
ITS+learning diagnostics
●
Summative writing
evaluation
●
Student forum monitoring
●
AI teaching assistants
●
Automatic test generation
●
Automatic test scoring
●
OER content
recommendation
●
Plagiarism detection
●
Student attention and
emotion detection
●
Educational data mining
for resource allocation
●
Diagnosing learning
difficulties (e.g. dyslexia)
●
Synthetic teachers
●
AI as a learning research
tool
Adapted from Holmes, W. et al. (2019)

28. Future is about change in the present
Education is a solution to social and economic problems.
To understand the impact of AI, we need to understand the
Industrial Age; what made the 20th Century so special?
●
Why jobs and “economic growth”?
●
Why need for skills and knowledge?
●
Why schools and teaching?
How the digital, global, and AI-enhanced world will be diferent?

29. JRC “AI for and with Teachers”
●
Co-creating future-oriented uses of AI in K-12 by integrating expertise in teaching,
educational innovation, policy, and AI technology
●
Exploratory work, with potential for a EU-wide initiative
●
Aims for an orchestrated network of forward-looking actors and stakeholders, and
produces an “AI Handbook for and with Teachers”
●
AI is high on the political agenda. We don’t want that teachers will be electrocuted
with it. Teachers need to be involved in inventing AI.
●
Policy-makers will soon realize that the core of national and international AI
strategies will be education. First they think it’s about teaching AI. Then they will
realize its about a shif in balance from teaching for employment to the
development of agency capabilities in the age of the AI.

30. Thank you!
ilkka.tuomi@meaningprocessing.com